Setup

library(ngsReports)
library(magrittr)
library(scales)
library(pander)
library(tidyverse)
theme_set(theme_bw())
options(scipen=999)
deMuxFqc <- list.files("../3_demuxTrimmed/FastQC/", pattern = "zip", full.names = TRUE) %>%
    getFastqcData()
alnFqc <- list.files("../4_aligned/FastQC/", pattern = "zip", full.names = TRUE) %>%
    getFastqcData()
oryGC <- read_rds("oryGC.RDS")

Compare Reads With Alignments

Alignments were filtered for:

*Comparison of library sizes before and after alignment. Given the filtering of alignments, this shows an acceptable rate of alignment across all samples*

Comparison of library sizes before and after alignment. Given the filtering of alignments, this shows an acceptable rate of alignment across all samples

*Alignment rate for each sample after filtering for uniquely mapped reads with high mapping qualities. The two samples gc2700 and gc2709 appear to be outliers.*

Alignment rate for each sample after filtering for uniquely mapped reads with high mapping qualities. The two samples gc2700 and gc2709 appear to be outliers.

Summary of Library Sizes After Alignment
Population Samples Smallest Library Median Library Largest Library Total Alignments
1996 59 1,541,386 5,253,757 16,146,550 351,966,386
2010 37 1,264,888 4,141,755 7,670,908 163,491,029
2012 53 3,411,367 6,740,247 14,398,379 377,550,470

Identification of Low Quality Samples by GC content

Difference in GC content when comparing each sample to Theoretical GC content from sampling the O. cuniculus genome.

GC content of the complete set of samples. The three outlier samples are clearly evident. 

lowQ <- paste(c("pt1125", "gc2709", "gc2700"), "bam", sep = ".")

Potential low quality samples were identified by GC content as pt1125.bam, gc2709.bam and gc2700.bam. Alignments from these samples should be moved and placed into a separate folder to ensure their exclusion from the stacks pipeline. The sample gc2776 was also of some concern, but was retained for downstream analysis.

Sequence Content

Inspection of the per base sequence content confirmed the divergent patterns of these samples.

*Per base sequence content of all samples, with divergent patterns clearly identifying the outlier samples*

Per base sequence content of all samples, with divergent patterns clearly identifying the outlier samples

Conclusion

All identified low quality samples will be manually moved to a sub-folder indicating their low quality and will be excluded from the stacks pipeline.

Session Info

R version 3.5.2 (2018-12-20)

**Platform:** x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_AU.UTF-8, LC_NUMERIC=C, LC_TIME=en_AU.UTF-8, LC_COLLATE=en_AU.UTF-8, LC_MONETARY=en_AU.UTF-8, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: bindrcpp(v.0.2.2), forcats(v.0.3.0), stringr(v.1.3.1), dplyr(v.0.7.8), purrr(v.0.2.5), readr(v.1.3.1), tidyr(v.0.8.2), tidyverse(v.1.2.1), pander(v.0.6.3), scales(v.1.0.0), magrittr(v.1.5), ngsReports(v.0.99.0), tibble(v.2.0.0), ggplot2(v.3.1.0), fastqcTheoreticalGC(v.0.0.1), BSgenome(v.1.50.0), rtracklayer(v.1.42.1), Biostrings(v.2.50.2), XVector(v.0.22.0), GenomicRanges(v.1.34.0), GenomeInfoDb(v.1.18.1), IRanges(v.2.16.0), S4Vectors(v.0.20.1) and BiocGenerics(v.0.28.0)

loaded via a namespace (and not attached): nlme(v.3.1-137), bitops(v.1.0-6), matrixStats(v.0.54.0), lubridate(v.1.7.4), RColorBrewer(v.1.1-2), httr(v.1.4.0), tools(v.3.5.2), backports(v.1.1.3), R6(v.2.3.0), lazyeval(v.0.2.1), colorspace(v.1.3-2), withr(v.2.1.2), tidyselect(v.0.2.5), compiler(v.3.5.2), cli(v.1.0.1), rvest(v.0.3.2), Biobase(v.2.42.0), Cairo(v.1.5-9), xml2(v.1.2.0), DelayedArray(v.0.8.0), plotly(v.4.8.0), ggdendro(v.0.1-20), labeling(v.0.3), digest(v.0.6.18), Rsamtools(v.1.34.0), rmarkdown(v.1.11), pkgconfig(v.2.0.2), htmltools(v.0.3.6), highr(v.0.7), htmlwidgets(v.1.3), rlang(v.0.3.0.1), readxl(v.1.2.0), rstudioapi(v.0.8), shiny(v.1.2.0), bindr(v.0.1.1), generics(v.0.0.2), zoo(v.1.8-4), hwriter(v.1.3.2), jsonlite(v.1.6), crosstalk(v.1.0.0), BiocParallel(v.1.16.5), RCurl(v.1.95-4.11), GenomeInfoDbData(v.1.2.0), Matrix(v.1.2-15), Rcpp(v.1.0.0), munsell(v.0.5.0), stringi(v.1.2.4), yaml(v.2.2.0), MASS(v.7.3-51.1), SummarizedExperiment(v.1.12.0), zlibbioc(v.1.28.0), plyr(v.1.8.4), grid(v.3.5.2), promises(v.1.0.1), crayon(v.1.3.4), lattice(v.0.20-38), haven(v.2.0.0), hms(v.0.4.2), knitr(v.1.21), pillar(v.1.3.1), reshape2(v.1.4.3), XML(v.3.98-1.16), glue(v.1.3.0), evaluate(v.0.12), ShortRead(v.1.40.0), latticeExtra(v.0.6-28), data.table(v.1.11.8), modelr(v.0.1.2), httpuv(v.1.4.5.1), cellranger(v.1.1.0), gtable(v.0.2.0), assertthat(v.0.2.0), xfun(v.0.4), mime(v.0.6), xtable(v.1.8-3), broom(v.0.5.1), later(v.0.7.5), viridisLite(v.0.3.0) and GenomicAlignments(v.1.18.1)